Intro to Data Prep for Machine Learning AlgorithmsReading time: 2 minutes
Data technology has become popular for a variety of reasons, chiefly because of the availability of tools that make it easy to collect and store data points. The current trend of big data is so impactful that the World Economic Forum is referring to this time period as the “Fourth Industrial Revolution.” Machine learning algorithms and predictive analytics are two of the major pieces of technology reshaping the world.
Machine learning and predictive analytics sound like complicated things, but the concept is actually very simple. Humans use models almost every minute of every day. The process of collecting and organizing information, and using that collection of information to make educated decisions about outcomes, is what we do as humans.
Supervised Machine Learning Algorithms
Every time you walk down a crowded street, your brain attempts to identify people and faces based on the data stored in your brain. In practice, I have memories/data points of all my friends and acquaintances; I use these data points to look for patterns that allow me to identify the people I know. When I see a person whose features match with my memories, I recognize that person. In the simplest of form, this is the idea of using data to classify information. Your memories of all of the people you know are simply a massive collection of data points – individual pieces of information that collectively make up an entire picture.
Obviously, the human brain can easily identify a friend walking down the street. But, what if you want to identify other things? What if you want to automatically group items together? What if you want to know the likelihood of the outcome of an event? Machine learning can answer all of these questions with the use of data. Machine learning uses data the same way we use data as humans. Machine learning algorithms take the arrangement of data points and, depending on the algorithm used, they generate a predicted outcome, or they make classifications. First, you need to prepare and organize the data.
Data Prep Steps
Just like memories that you recall inaccurately, data can be fraught with inaccuracy, missing values, and sometimes lack of clarity. Data prep is a process that may involve one or several of the following:
- Identifying and removing duplicate records
- Handling missing values
- Identifying and handling outliers
- Merging data from multiple disparate sources
- Aggregating and consolidating data
- Filtering and extracting the appropriate data
The data prep tasks themselves can be daunting to perform, particularly if you do not have the right tools. The lack of appropriate tools can drag down your efficiency, eating up your time and other resources. You can run into difficulties with data prep if you:
- Rely on an inefficient process that is dependent upon hard coding queries and scripts;
- Apply these commands directly to the data source and permanently alter the original data;
- Move the data from one database to another location to perform that data prep process.
Prep Data Easily
There is a lot of ground to cover with these data prep tasks. With Construct, you can fast track to the process with a code-free, self-service solution that brings accuracy and repeatability to the forefront of your work. You can iterate and automate your data prep process, giving you more time to focus on discovering insights through your existing machine learning algorithms.